The discovery of algorithmic probability
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Handbook of Video Databases: Design and Applications
Handbook of Video Databases: Design and Applications
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Algorithmic information theory
IBM Journal of Research and Development
Attractor memory with self-organizing input
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Does a plane imitate a bird? does computer vision have to follow biological paradigms?
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
IEEE Transactions on Information Theory
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
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Image understanding and image semantics processing have recently become an issue of critical importance in computer vision R&D. Biological vision has always considered them as an enigmatic mixture of perceptual and cognitive processing faculties. In its impetuous and rash development, computer vision without any hesitations has adopted this stance. I will argue that such a segregation of image processing faculties is wrong, both for the biological and the computer vision. My conjecture is that images contain only one sort of information - the perceptual (physical) information, which can be discovered in an image and elicited for further processing. Cognitive (semantic) information is not a part of image-conveyed information. It belongs to a human observer that acquires and interprets the image. Relying on a new definition of "information", which can be derived from Kolmogorov's complexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for perceptual and cognitive image processing peculiarities. I believe, it would provide better scaffolding for modeling visual information processing in human brain.